bioRxivCancer BiologyPreprint
Elucidation of putative key genes involved in the regulation of triple negative breast cancer development and progression
Alok KumariD, Gauri Shankar UpadhyayiD, Mohammad KashifiD, Md. Zubbair MalikiD, Naidu SubbaraoiD, Maitreyi S. RajalaiD
Abstract
The molecular basis of triple-negative breast cancer (TNBC), a highly aggressive and therapy-resistant subtype of breast cancer, is poorly understood. This study aims to identify key genes and pathways involved in TNBC development and progression using a systems biology approach followed by experimental validation. Here, two transcriptome microarray datasets from the GEO database were analysed using the R package LIMMA to detect differentially expressed genes (DEGs) in TNBC tumors. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analyses using the DAVID database were performed to identify DEGs regulated biological functions and pathways. Further, a protein-protein interaction (PPI) network was constructed using the STRING online database, and the topological properties were determined using MCODE and Cytohubba plug-ins. The expression and the prognostic value of the hub genes were validated using the Cancer Genome Atlas (TCGA) survival analysis. We found 727 DEGs, of which 473 were downregulated and 254 were upregulated in TNBC vs. non-TNBC samples. The GO and KEGG analyses indicated that the DEGs were mainly related to cell adhesion, tumorigenesis, and cellular immunity. The PPI network had shown six hub genes, namely CCND1, CDH1, ESR1, FN1, IL6, and PPARG, as the top key regulators. All these genes were validated by quantitative real-time PCR in the TNBC cell line using non-TNBC cell line as a calibrator, and the obtained results were in accordance with the bioinformatics data. This information may contribute to understanding the various molecular mechanisms that drive the development and progression of TNBC tumors.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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